53 papers
This paper proposes a framework using Poisson regression with censored data filtering to accurately estimate excess demand in public transportation systems, addressing the common issue of underestimation caused by unrecorded passengers left behind on full buses, and validates the approach using simulated data and real-world data from Pittsburgh's Port Authority.
By modeling US House elections as a random field Ising system, this paper empirically validates a critical spending threshold of approximately $1.8 million that triggers a phase transition to high political polarization, where election outcomes become determined by voter alignment rather than total campaign expenditure.
This paper proposes a method to distinguish between organic and artificial rumor virality by analyzing oscillatory patterns in autocorrelation using the modified Maki-Thompson model, while also demonstrating how targeted network interventions can manipulate a rumor's lifespan.
This paper demonstrates that on Geometric Inhomogeneous Random Graphs, majority-vote opinion dynamics lead to the stabilization of localized opinion domains rather than global consensus, a phenomenon rigorously explained by a mean-field model showing a stationary interface profile that supports persistent opinion diversity.
This paper proposes a novel approach using persistent homology on a filtered witness complex to identify geographic gaps in cooling center coverage across four US cities, demonstrating that combining these topological findings with traditional heat vulnerability indices provides a more holistic assessment of heat-related mortality risk.
This paper proposes a unified methodological framework, drawing from diverse scientific fields such as soft matter, ecology, and machine learning, to analyze and redesign complex food systems in order to better predict critical transitions and optimize interventions for mitigating climate change.
This paper presents a unified framework that reconstructs the multilayer bank-firm network structure of the Italian economy from balance sheet data to simulate shock propagation and identify systemic risks, enabling detailed stress testing without relying on inaccessible, privacy-protected network information.
GradNet introduces a gradient-based, AI-enabled framework that inverts traditional network science by treating topology as a continuously differentiable object to optimize arbitrary dynamical objectives under constraints, thereby demonstrating that canonical network features emerge spontaneously from constrained optimization rather than explicit design.
This paper demonstrates that a portable, two-meter neutron Time of Flight system utilizing Neutron Resonance Transmission Analysis can successfully identify and quantify the isotopic composition of special nuclear materials like HEU and reactor-grade plutonium within two hours with high accuracy, offering a promising tool for future arms control verification.
This paper establishes a novel "Spectral Portfolio Theory" that identifies neural network weight matrices trained via stochastic gradient descent as portfolio allocation matrices, demonstrating how their spectral evolution from Marchenko-Pastur to inverse-Wishart statistics unifies diverse wealth dynamics models and yields a Spectral Invariance Theorem with applications in portfolio design, inequality measurement, and tax policy.
This paper analyzes all 960 Chess960 starting positions using Stockfish evaluations and a novel information-based metric to reveal a universal White first-move advantage and a highly heterogeneous landscape of strategic complexity and decision asymmetry, demonstrating that the classical chess setup is merely one configuration among many rather than a unique extremum.
This paper reviews the limitations of static urban models and advocates for dynamic, empirically grounded frameworks based on partial differential equations to better capture the complex, non-equilibrium spatial dynamics of urban sprawl driven by population, infrastructure, and market factors.
This study employs Bayesian structural time-series causal inference on multi-decadal satellite data to demonstrate that the rapid expansion of nickel processing facilities at Indonesia's Morowali Industrial Park has caused a significant, quantifiable degradation of coastal water clarity, posing potential risks to the region's high-biodiversity marine ecosystems.
By applying geometric branch growth and renormalization to real mouse suprachiasmatic nucleus networks, this study reveals that circadian rhythms remain robust across different network scales, demonstrating that average connectivity degree, rather than network size or clustering, is the primary structural driver of rhythmic stability.
This paper applies thermodynamic and economic principles to reframe AI tokens as physical quantities with measurable costs, establishing a finite global "question budget" to argue that the critical challenge for humanity is not the volume of computable answers but the agency required to determine which questions are worth asking.
This paper utilizes stochastic modeling and Boston University data to predict that the proposed 40% federal research funding cuts under the 2026 Trump Administration would drastically increase the proportion of R1 universities where over half of their faculty face subcritical annual expenditures, thereby threatening the viability of quality research and doctoral programs across the United States.
This paper presents exactly solvable quantum models of opinion dynamics that leverage superposition, entanglement, and measurement-induced collapse to simulate consensus formation on networks, with practical validation on IBM Quantum devices demonstrating the potential of near-term quantum computers for social modeling.
A quasi-experimental study in a high school physics course found that while using tablets and smartphones as experimental tools did not significantly outperform conventional teaching methods in terms of learning gains or motivation, it proved to be an effective alternative that achieved comparable results without causing negative effects like distraction or cognitive overload.
This survey provides a structured synthesis of interdisciplinary research on algorithmic interventions in opinion dynamics, organizing existing work by optimization objectives, reviewing mathematical formulations and algorithms, and outlining future directions for fostering healthier online environments.
This study reveals that while Brasília's modernist urban form appears integrated at coarse scales, it actually fosters significant segregation at the block level where daily interactions occur, driven more by enclosed residential designs and barriers than by visit volumes, suggesting that true integration requires distributing shared destinations and porous connections rather than sequestering them.